Master Data Prep & Descriptive Stats: Outliers, Missing Values, + More! | PD Model Dev - 1

In this video, we dive deep into the crucial aspects of data preparation for predictive modeling. Learn how to master the art of handling descriptive statistics, detecting and treating outliers, dealing with missing values, and more! 📊 Key Topics Covered: Understanding Descriptive Statistics Identifying and Treating Outliers Strategies for Handling Missing Values Essential Data Manipulation Techniques 🌐 Series Overview: This video is the first instalment in our comprehensive PD Model Development Series. Whether you're a beginner or an experienced data scientist, this series will equip you with the skills needed to build robust predictive models. 🎓 Who is This For? Data Scientists and Analysts Machine Learning Enthusiasts Financial Analysts and Risk Managers Anyone Interested in Predictive Modelling 👍 Engage With Us: If you find this content valuable, don't forget to like, share, and subscribe for more in-depth tutorials on predictive modelling and data science. Share your thoughts and questions in the comments below – we love hearing from our community! You may also like to watch:- What is probability of default - https://youtu.be/N9rmiGFzSnw?si=2xO6QPHM7iwOT3EO Exploratory Data Analysis - https://youtu.be/fRVXW_oCTj0?si=ifvEe-gk-47Y0F4N Data Science Playlist - https://youtube.com/playlist?list=PL4GjoPPG4VqOmyh7hQ730evtLaz04LwSf&si=uAlA4E-8MYnqzDtW Pandas Full Playlist - https://youtube.com/playlist?list=PL4GjoPPG4VqP0BNdnIZQqjCYodmtFCcee NumPy Full Playlist - https://youtube.com/playlist?list=PL4GjoPPG4VqNz8W3JRDD-uRcM9WVJHYve Matplotlib Full Playlist - https://youtube.com/playlist?list=PL4GjoPPG4VqN4dsP39m4QfpZSUrv2y8Iu Seaborn Full Playlist - https://youtube.com/playlist?list=PL4GjoPPG4VqOAwSNw2I-PXUcjw1frHmW2 📌 PD Model Dev Series Playlist: https://www.youtube.com/playlist?list=PL4GjoPPG4VqOICQi3Zo7Yxo2Fe3zU8MfN Resources - Developed script in the video - https://github.com/LEARNEREA/Data_Science/blob/main/Scripts/Credit_Risk_PD_Model.ipynb Script to understand the diff. in scores - https://github.com/LEARNEREA/Data_Science/blob/main/Scripts/Understand_differences_in_numbers.ipynb Automated Pre-processing - https://github.com/LEARNEREA/Data_Science/blob/main/Scripts/PreProcessing_PD_Model.py Logistic saved model - https://github.com/LEARNEREA/Data_Science/blob/main/Scripts/logisticPDmodel.pkl Random Forest saved model - https://github.com/LEARNEREA/Data_Science/blob/main/Scripts/RandomForesPDmodel.zip XGB saved model - https://github.com/LEARNEREA/Data_Science/blob/main/Scripts/XGBpdModel.pkl Raw data utilised in the development - https://github.com/LEARNEREA/Data_Science/blob/main/Data/credit_risk_dataset.csv 🌐 Connect with Us: Follow us on social media for behind-the-scenes content, updates, and more: Facebook: https://www.facebook.com/Learnerea/ LinkedIn: https://www.linkedin.com/company/learnerea/ Ready to elevate your data science skills? Watch now and embark on a journey to become a PD modelling expert! #DataScience #PredictiveModeling #DataPreparation #PDModelDevelopment #DataAnalytics #MachineLearning